Overview

Dataset statistics

Number of variables18
Number of observations14934
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 MiB
Average record size in memory140.0 B

Variable types

Categorical10
Numeric8

Alerts

atemp is highly overall correlated with season and 1 other fieldsHigh correlation
bad_weather is highly overall correlated with weathersit_MistHigh correlation
cnt is highly overall correlated with hrHigh correlation
hr is highly overall correlated with cnt and 1 other fieldsHigh correlation
is_peak_hour is highly overall correlated with hrHigh correlation
is_weekend is highly overall correlated with weekdayHigh correlation
month is highly overall correlated with seasonHigh correlation
season is highly overall correlated with atemp and 1 other fieldsHigh correlation
weather_comfort is highly overall correlated with atempHigh correlation
weathersit_Mist is highly overall correlated with bad_weatherHigh correlation
weekday is highly overall correlated with is_weekend and 1 other fieldsHigh correlation
workingday is highly overall correlated with weekdayHigh correlation
holiday is highly imbalanced (81.7%)Imbalance
weathersit_Heavy Rain is highly imbalanced (99.7%)Imbalance
weathersit_Light Snow is highly imbalanced (57.5%)Imbalance
hr has 721 (4.8%) zerosZeros
weekday has 1994 (13.4%) zerosZeros

Reproduction

Analysis started2026-01-11 08:38:09.290045
Analysis finished2026-01-11 08:38:18.563689
Duration9.27 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

season
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size233.3 KiB
1
3910 
4
3710 
3
3680 
2
3634 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14934
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
13910
26.2%
43710
24.8%
33680
24.6%
23634
24.3%

Length

2026-01-11T14:08:18.706125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-11T14:08:18.777482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
13910
26.2%
43710
24.8%
33680
24.6%
23634
24.3%

Most occurring characters

ValueCountFrequency (%)
13910
26.2%
43710
24.8%
33680
24.6%
23634
24.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
13910
26.2%
43710
24.8%
33680
24.6%
23634
24.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
13910
26.2%
43710
24.8%
33680
24.6%
23634
24.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
13910
26.2%
43710
24.8%
33680
24.6%
23634
24.3%

hr
Real number (ℝ)

High correlation  Zeros 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.091737
Minimum0
Maximum23
Zeros721
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size233.3 KiB
2026-01-11T14:08:18.846994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.1764845
Coefficient of variation (CV)0.64701178
Kurtosis-1.2500922
Mean11.091737
Median Absolute Deviation (MAD)6
Skewness0.13007241
Sum165644
Variance51.50193
MonotonicityNot monotonic
2026-01-11T14:08:19.185974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0721
 
4.8%
23721
 
4.8%
6719
 
4.8%
1718
 
4.8%
22712
 
4.8%
21711
 
4.8%
5711
 
4.8%
2708
 
4.7%
9703
 
4.7%
20691
 
4.6%
Other values (14)7819
52.4%
ValueCountFrequency (%)
0721
4.8%
1718
4.8%
2708
4.7%
3689
4.6%
4687
4.6%
5711
4.8%
6719
4.8%
7660
4.4%
8481
3.2%
9703
4.7%
ValueCountFrequency (%)
23721
4.8%
22712
4.8%
21711
4.8%
20691
4.6%
19564
3.8%
18371
2.5%
17311
2.1%
16555
3.7%
15558
3.7%
14551
3.7%

holiday
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size233.3 KiB
0
14519 
1
 
415

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14934
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014519
97.2%
1415
 
2.8%

Length

2026-01-11T14:08:19.320977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-11T14:08:19.379124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
014519
97.2%
1415
 
2.8%

Most occurring characters

ValueCountFrequency (%)
014519
97.2%
1415
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
014519
97.2%
1415
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
014519
97.2%
1415
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
014519
97.2%
1415
 
2.8%

weekday
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9931699
Minimum0
Maximum6
Zeros1994
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size233.3 KiB
2026-01-11T14:08:19.428883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9604559
Coefficient of variation (CV)0.65497648
Kurtosis-1.2180404
Mean2.9931699
Median Absolute Deviation (MAD)2
Skewness-0.0019199438
Sum44700
Variance3.8433874
MonotonicityNot monotonic
2026-01-11T14:08:19.488373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
52230
14.9%
32209
14.8%
42195
14.7%
12193
14.7%
22182
14.6%
01994
13.4%
61931
12.9%
ValueCountFrequency (%)
01994
13.4%
12193
14.7%
22182
14.6%
32209
14.8%
42195
14.7%
52230
14.9%
61931
12.9%
ValueCountFrequency (%)
61931
12.9%
52230
14.9%
42195
14.7%
32209
14.8%
22182
14.6%
12193
14.7%
01994
13.4%

workingday
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size233.3 KiB
1
10596 
0
4338 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14934
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
110596
71.0%
04338
29.0%

Length

2026-01-11T14:08:19.583199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-11T14:08:19.643069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
110596
71.0%
04338
29.0%

Most occurring characters

ValueCountFrequency (%)
110596
71.0%
04338
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
110596
71.0%
04338
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
110596
71.0%
04338
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
110596
71.0%
04338
29.0%

atemp
Real number (ℝ)

High correlation 

Distinct65
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.1826803 × 10-17
Minimum-2.7036975
Maximum3.1687995
Zeros0
Zeros (%)0.0%
Negative7506
Negative (%)50.3%
Memory size233.3 KiB
2026-01-11T14:08:19.706091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2.7036975
5-th percentile-1.5468156
Q1-0.83506895
median-0.034647614
Q30.85562292
95-th percentile1.5673696
Maximum3.1687995
Range5.872497
Interquartile range (IQR)1.6906919

Descriptive statistics

Standard deviation1.0000335
Coefficient of variation (CV)-4.5816763 × 1016
Kurtosis-0.81621115
Mean-2.1826803 × 10-17
Median Absolute Deviation (MAD)0.80100858
Skewness0.015406096
Sum-9.8587805 × 10-14
Variance1.000067
MonotonicityNot monotonic
2026-01-11T14:08:19.898227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9442976281702
 
4.7%
-0.7463942448575
 
3.9%
-0.3012589758548
 
3.7%
0.321812951540
 
3.6%
-0.9243309025532
 
3.6%
0.2325509973493
 
3.3%
0.8556229241492
 
3.3%
-0.03464761406490
 
3.3%
-0.2125842718488
 
3.3%
0.410487655483
 
3.2%
Other values (55)9591
64.2%
ValueCountFrequency (%)
-2.703697482
 
< 0.1%
-2.6144355264
 
< 0.1%
-2.5257608228
 
0.1%
-2.4364988687
 
< 0.1%
-2.34782416413
 
0.1%
-2.2585622124
 
0.2%
-2.16988750611
 
0.1%
-2.08062555335
 
0.2%
-1.99195084978
0.5%
-1.90268889588
0.6%
ValueCountFrequency (%)
3.1687994741
 
< 0.1%
3.0795375212
 
< 0.1%
2.9016008631
 
< 0.1%
2.7236642054
 
< 0.1%
2.6349895015
 
< 0.1%
2.54572754714
 
0.1%
2.45705284316
0.1%
2.3677908916
0.1%
2.27911618623
0.2%
2.18985423239
0.3%

windspeed
Real number (ℝ)

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum-1.6061386
Maximum2.520559
Zeros0
Zeros (%)0.0%
Negative7782
Negative (%)52.1%
Memory size233.3 KiB
2026-01-11T14:08:19.971415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.6061386
5-th percentile-1.6061386
Q1-0.67413108
median-0.14168273
Q30.65654384
95-th percentile1.8552215
Maximum2.520559
Range4.1266976
Interquartile range (IQR)1.3306749

Descriptive statistics

Standard deviation1.0000335
Coefficient of variation (CV)nan
Kurtosis-0.38338217
Mean0
Median Absolute Deviation (MAD)0.66533746
Skewness0.25901242
Sum1.1368684 × 10-13
Variance1.000067
MonotonicityNot monotonic
2026-01-11T14:08:20.032375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
-1.6061386481977
13.2%
-0.40835284331565
10.5%
-0.14168273481482
9.9%
-0.67413107851458
9.8%
0.12409550031457
9.8%
-0.8070201961300
8.7%
0.39076560881284
8.6%
0.6565438441094
7.3%
0.9232139524881
5.9%
1.05610307666
 
4.5%
Other values (6)1770
11.9%
ValueCountFrequency (%)
-1.6061386481977
13.2%
-0.8070201961300
8.7%
-0.67413107851458
9.8%
-0.40835284331565
10.5%
-0.14168273481482
9.9%
0.12409550031457
9.8%
0.39076560881284
8.6%
0.6565438441094
7.3%
0.9232139524881
5.9%
1.05610307666
 
4.5%
ValueCountFrequency (%)
2.520558983132
 
0.9%
2.387669866156
 
1.0%
2.120999757249
 
1.7%
1.855221522319
 
2.1%
1.588551414394
 
2.6%
1.322773178520
3.5%
1.05610307666
4.5%
0.9232139524881
5.9%
0.6565438441094
7.3%
0.39076560881284
8.6%

cnt
Real number (ℝ)

High correlation 

Distinct505
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.52123
Minimum1
Maximum508
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size233.3 KiB
2026-01-11T14:08:20.149028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q131
median115
Q3221
95-th percentile379.35
Maximum508
Range507
Interquartile range (IQR)190

Descriptive statistics

Standard deviation121.3373
Coefficient of variation (CV)0.86348022
Kurtosis-0.22356588
Mean140.52123
Median Absolute Deviation (MAD)91
Skewness0.7855814
Sum2098544
Variance14722.74
MonotonicityNot monotonic
2026-01-11T14:08:20.282777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5257
 
1.7%
6234
 
1.6%
4229
 
1.5%
3219
 
1.5%
2205
 
1.4%
7195
 
1.3%
8182
 
1.2%
10154
 
1.0%
1149
 
1.0%
11145
 
1.0%
Other values (495)12965
86.8%
ValueCountFrequency (%)
1149
1.0%
2205
1.4%
3219
1.5%
4229
1.5%
5257
1.7%
6234
1.6%
7195
1.3%
8182
1.2%
9128
0.9%
10154
1.0%
ValueCountFrequency (%)
5081
 
< 0.1%
5061
 
< 0.1%
5054
< 0.1%
5041
 
< 0.1%
5031
 
< 0.1%
5021
 
< 0.1%
5002
 
< 0.1%
4994
< 0.1%
4981
 
< 0.1%
4975
< 0.1%

day
Real number (ℝ)

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.710125
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size175.0 KiB
2026-01-11T14:08:20.421097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29.35
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8058447
Coefficient of variation (CV)0.56052036
Kurtosis-1.1901386
Mean15.710125
Median Absolute Deviation (MAD)8
Skewness0.0077520635
Sum234615
Variance77.5429
MonotonicityNot monotonic
2026-01-11T14:08:20.564692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1519
 
3.5%
7505
 
3.4%
6504
 
3.4%
21504
 
3.4%
13500
 
3.3%
20500
 
3.3%
24500
 
3.3%
16499
 
3.3%
8498
 
3.3%
14496
 
3.3%
Other values (21)9909
66.4%
ValueCountFrequency (%)
1519
3.5%
2490
3.3%
3477
3.2%
4469
3.1%
5491
3.3%
6504
3.4%
7505
3.4%
8498
3.3%
9492
3.3%
10468
3.1%
ValueCountFrequency (%)
31299
2.0%
30448
3.0%
29456
3.1%
28483
3.2%
27472
3.2%
26486
3.3%
25487
3.3%
24500
3.3%
23485
3.2%
22496
3.3%

month
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5088389
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size175.0 KiB
2026-01-11T14:08:20.690962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.5296179
Coefficient of variation (CV)0.54228072
Kurtosis-1.254079
Mean6.5088389
Median Absolute Deviation (MAD)3
Skewness0.0038547677
Sum97203
Variance12.458202
MonotonicityNot monotonic
2026-01-11T14:08:20.779609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
121403
9.4%
11368
9.2%
111290
8.6%
31279
8.6%
21231
8.2%
71222
8.2%
81220
8.2%
51219
8.2%
101208
8.1%
41192
8.0%
Other values (2)2302
15.4%
ValueCountFrequency (%)
11368
9.2%
21231
8.2%
31279
8.6%
41192
8.0%
51219
8.2%
61155
7.7%
71222
8.2%
81220
8.2%
91147
7.7%
101208
8.1%
ValueCountFrequency (%)
121403
9.4%
111290
8.6%
101208
8.1%
91147
7.7%
81220
8.2%
71222
8.2%
61155
7.7%
51219
8.2%
41192
8.0%
31279
8.6%

year
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size233.3 KiB
2011
7846 
2012
7088 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters59736
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2011
2nd row2011
3rd row2011
4th row2011
5th row2011

Common Values

ValueCountFrequency (%)
20117846
52.5%
20127088
47.5%

Length

2026-01-11T14:08:20.890025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-11T14:08:20.988494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
20117846
52.5%
20127088
47.5%

Most occurring characters

ValueCountFrequency (%)
122780
38.1%
222022
36.9%
014934
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)59736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
122780
38.1%
222022
36.9%
014934
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)59736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
122780
38.1%
222022
36.9%
014934
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)59736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
122780
38.1%
222022
36.9%
014934
25.0%

is_peak_hour
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size233.3 KiB
0
11844 
1
3090 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14934
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011844
79.3%
13090
 
20.7%

Length

2026-01-11T14:08:21.094947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-11T14:08:21.182633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
011844
79.3%
13090
 
20.7%

Most occurring characters

ValueCountFrequency (%)
011844
79.3%
13090
 
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
011844
79.3%
13090
 
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
011844
79.3%
13090
 
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
011844
79.3%
13090
 
20.7%

is_weekend
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size233.3 KiB
0
10773 
1
4161 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14934
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
010773
72.1%
14161
 
27.9%

Length

2026-01-11T14:08:21.316377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-11T14:08:21.370358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
010773
72.1%
14161
 
27.9%

Most occurring characters

ValueCountFrequency (%)
010773
72.1%
14161
 
27.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
010773
72.1%
14161
 
27.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
010773
72.1%
14161
 
27.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
010773
72.1%
14161
 
27.9%

weather_comfort
Real number (ℝ)

High correlation 

Distinct856
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5675708 × 10-17
Minimum-1.3692525
Maximum5.1621944
Zeros0
Zeros (%)0.0%
Negative8918
Negative (%)59.7%
Memory size233.3 KiB
2026-01-11T14:08:21.431424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.3692525
5-th percentile-1.1660519
Q1-0.75965079
median-0.23390963
Q30.46922884
95-th percentile2.0270999
Maximum5.1621944
Range6.5314469
Interquartile range (IQR)1.2288796

Descriptive statistics

Standard deviation1.0000335
Coefficient of variation (CV)2.1894209 × 1016
Kurtosis1.5095246
Mean4.5675708 × 10-17
Median Absolute Deviation (MAD)0.56767143
Skewness1.2289561
Sum3.6959324 × 10-13
Variance1.000067
MonotonicityNot monotonic
2026-01-11T14:08:21.525244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.369252496267
 
1.8%
-0.3048685626133
 
0.9%
-0.5951550899133
 
0.9%
-0.8854416172127
 
0.9%
-0.7886794414126
 
0.8%
-0.8015810649105
 
0.7%
-0.4645261526104
 
0.7%
-0.5193580522102
 
0.7%
-0.4661388555101
 
0.7%
-0.198430169294
 
0.6%
Other values (846)13642
91.3%
ValueCountFrequency (%)
-1.369252496267
1.8%
-1.3079697852
 
< 0.1%
-1.3015189732
 
< 0.1%
-1.2918427551
 
< 0.1%
-1.2902300537
 
< 0.1%
-1.2853919445
 
< 0.1%
-1.27894113218
 
0.1%
-1.2789411326
 
< 0.1%
-1.2741030233
 
< 0.1%
-1.2676522128
 
0.1%
ValueCountFrequency (%)
5.1621943691
 
< 0.1%
4.8622316241
 
< 0.1%
4.7880472891
 
< 0.1%
4.7138629541
 
< 0.1%
4.359068312
 
< 0.1%
4.1461915231
 
< 0.1%
4.1203882761
 
< 0.1%
4.1187755731
 
< 0.1%
4.0945850291
 
< 0.1%
4.0736198916
< 0.1%

weathersit_Heavy Rain
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size233.3 KiB
0
14931 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14934
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014931
> 99.9%
13
 
< 0.1%

Length

2026-01-11T14:08:21.720424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-11T14:08:21.814363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
014931
> 99.9%
13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
014931
> 99.9%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
014931
> 99.9%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
014931
> 99.9%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
014931
> 99.9%
13
 
< 0.1%

weathersit_Light Snow
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size233.3 KiB
0
13642 
1
 
1292

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14934
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
013642
91.3%
11292
 
8.7%

Length

2026-01-11T14:08:21.925890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-11T14:08:22.003194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
013642
91.3%
11292
 
8.7%

Most occurring characters

ValueCountFrequency (%)
013642
91.3%
11292
 
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
013642
91.3%
11292
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
013642
91.3%
11292
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
013642
91.3%
11292
 
8.7%

weathersit_Mist
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size233.3 KiB
0
10861 
1
4073 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14934
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010861
72.7%
14073
 
27.3%

Length

2026-01-11T14:08:22.099988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-11T14:08:22.153090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
010861
72.7%
14073
 
27.3%

Most occurring characters

ValueCountFrequency (%)
010861
72.7%
14073
 
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
010861
72.7%
14073
 
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
010861
72.7%
14073
 
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
010861
72.7%
14073
 
27.3%

bad_weather
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size233.3 KiB
0
9569 
1
5365 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14934
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09569
64.1%
15365
35.9%

Length

2026-01-11T14:08:22.259228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-11T14:08:22.315811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
09569
64.1%
15365
35.9%

Most occurring characters

ValueCountFrequency (%)
09569
64.1%
15365
35.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
09569
64.1%
15365
35.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
09569
64.1%
15365
35.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14934
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
09569
64.1%
15365
35.9%

Interactions

2026-01-11T14:08:17.377787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:10.751597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:11.914763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:12.795765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:13.694357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:14.563720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:15.582797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:16.502458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:17.468554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:10.859531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:11.987538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:12.904326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:13.799515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:14.689755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:15.732693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:16.588600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:17.593736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:10.987411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:12.111295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:12.995108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:13.872514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:14.850511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:15.840846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:16.665823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:17.708867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:11.092758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:12.221848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:13.089198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:13.962174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:14.940197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:15.936006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:16.800727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:17.791920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:11.232893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:12.317520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:13.193149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:14.046908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:15.065161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:16.023496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:16.915107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:17.912618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:11.623212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:12.465753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:13.302449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:14.171758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:15.206905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:16.110218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:17.046128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:17.999239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:11.747927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:12.561199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:13.462599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:14.317044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:15.341721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:16.274562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:17.173705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:18.108056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:11.834185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:12.662256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:13.591632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:14.433741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:15.449212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:16.393812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-11T14:08:17.286012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-11T14:08:22.372304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
atempbad_weathercntdayholidayhris_peak_houris_weekendmonthseasonweather_comfortweathersit_Heavy Rainweathersit_Light Snowweathersit_Mistweekdaywindspeedworkingdayyear
atemp1.0000.1800.3540.0230.0680.0870.0730.0790.2090.5150.5740.0340.1390.112-0.009-0.0640.1380.080
bad_weather0.1801.0000.0820.0810.0080.0850.0350.0060.1380.0970.3310.0000.4110.8180.0420.0290.0260.000
cnt0.3540.0821.000-0.0080.0490.5460.3830.0400.1190.1430.4870.0000.1230.0300.0360.1180.1700.191
day0.0230.081-0.0081.0000.0580.0050.0000.0260.0140.0300.0030.0000.0340.074-0.0050.0150.0350.000
holiday0.0680.0080.0490.0581.0000.0150.0170.0640.1080.0620.0560.0000.0180.0000.2700.0210.2620.010
hr0.0870.0850.5460.0050.0151.0000.9340.062-0.0110.0540.2620.0060.0630.086-0.0070.1280.1470.084
is_peak_hour0.0730.0350.3830.0000.0170.9341.0000.0350.0570.0520.0550.0000.0340.0140.0750.0450.0770.076
is_weekend0.0790.0060.0400.0260.0640.0620.0351.0000.0220.0110.0490.0000.0090.0001.0000.0250.2520.008
month0.2090.1380.1190.0140.108-0.0110.0570.0221.0000.8870.0300.0190.0830.1020.011-0.1250.0910.024
season0.5150.0970.1430.0300.0620.0540.0520.0110.8871.0000.2440.0190.0540.0740.0290.0980.0730.030
weather_comfort0.5740.3310.4870.0030.0560.2620.0550.0490.0300.2441.0000.0000.3070.1810.0160.1940.1460.087
weathersit_Heavy Rain0.0340.0000.0000.0000.0000.0060.0000.0000.0190.0190.0001.0000.0000.0000.0000.0000.0000.000
weathersit_Light Snow0.1390.4110.1230.0340.0180.0630.0340.0090.0830.0540.3070.0001.0000.1880.0600.0650.0240.013
weathersit_Mist0.1120.8180.0300.0740.0000.0860.0140.0000.1020.0740.1810.0000.1881.0000.0310.0420.0100.013
weekday-0.0090.0420.036-0.0050.270-0.0070.0751.0000.0110.0290.0160.0000.0600.0311.0000.0010.9370.000
windspeed-0.0640.0290.1180.0150.0210.1280.0450.025-0.1250.0980.1940.0000.0650.0420.0011.0000.0270.035
workingday0.1380.0260.1700.0350.2620.1470.0770.2520.0910.0730.1460.0000.0240.0100.9370.0271.0000.000
year0.0800.0000.1910.0000.0100.0840.0760.0080.0240.0300.0870.0000.0130.0130.0000.0350.0001.000

Missing values

2026-01-11T14:08:18.312706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-11T14:08:18.470145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

seasonhrholidayweekdayworkingdayatempwindspeedcntdaymonthyearis_peak_houris_weekendweather_comfortweathersit_Heavy Rainweathersit_Light Snowweathersit_Mistbad_weather
010060-1.013006-1.6061391611201101-1.0015560000
111060-1.102268-1.6061394011201101-1.0144580000
212060-1.102268-1.6061393211201101-0.7128820000
313060-1.013006-1.6061391311201101-0.8854420000
414060-1.013006-1.606139111201101-0.8854420000
515060-1.190942-0.807020111201101-0.8854420011
616060-1.102268-1.606139211201101-1.0144580000
717060-1.190942-1.606139311201111-1.1434740000
818060-1.013006-1.606139811201111-0.8854420000
919060-0.657132-1.6061391411201111-0.4016310000
seasonhrholidayweekdayworkingdayatempwindspeedcntdaymonthyearis_peak_houris_weekendweather_comfortweathersit_Heavy Rainweathersit_Light Snowweathersit_Mistbad_weather
17369114011-1.1022680.3907662473112201200-0.1274710011
17370115011-1.013006-0.4083533153112201200-0.1274710011
173711160110.1432890.1240962143112201200-0.2790650011
17372117011-1.013006-0.8070201643112201210-0.2790650011
17373118011-1.102268-0.4083531223112201210-0.2790650011
17374119011-1.190942-0.1416831193112201210-0.5306470011
17375120011-1.190942-0.141683893112201200-0.5306470011
17376121011-1.190942-0.1416839031122012000.2434500000
17377122011-1.102268-0.408353613112201200-0.4467860000
17378123011-1.102268-0.408353493112201200-0.6354730000